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Data clustering-based approach for optimal capacitor allocation in distribution systems including wind farms

机译:基于数据聚类的分配系统最优电容分配方法,包括风电场

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摘要

Optimal placement and sizing of shunt capacitors are important issues to improve voltage profile and reduce power losses in distribution networks. In this regard, dealing with stochastic nature of wind farms (WFs), the issue becomes more complex in wind-integrated radial distribution networks. This study aims at optimal siting and sizing of shunt capacitors considering wind uncertainty. Application of probabilistic approach causes to access more real and extensive information about the network. To do so, Monte Carlo simulation (MCS) is employed to assess stochastic variation of wind farm output. As the salient feature of this research, K-means-based data clustering approach is utilised, in which all data-points of the output power of WFs are bunched into desired clusters to ease the solving of simple MCS. The voltage profile and power losses are analysed to do the optimal siting and sizing of capacitors. In order to assess the effectiveness of the proposed method, results are compared with the simple MCS that proves the efficiency of the proposed method from the viewpoint of computation time.
机译:分流电容器的最佳放置和尺寸是改善电压曲线的重要问题,并降低分配网络中的功率损耗。在这方面,处理风电场的随机性质(WFS),在风集成的径向分配网络中发出变得更加复杂。本研究旨在考虑风力不确定性的最佳选址和尺寸的分流电容器。概率方法的应用导致访问网络的更真实和广泛信息。为此,使用Monte Carlo仿真(MCS)来评估风电场输出的随机变化。作为本研究的突出特征,利用了基于K-Means的数据聚类方法,其中WFS的输出功率的所有数据点都被捆绑成所需的集群,以便于求解简单MCS。分析电压曲线和功率损耗以进行电容器的最佳选址和尺寸。为了评估所提出的方法的有效性,将结果与简单的MCS比较,从计算时间看出了所提出的方法的效率。

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